{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# In-Depth: Decision Trees and Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Previously\n",
    "\n",
    "- simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) \n",
    "- powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "## *Random Forests*\n",
    "- Another powerful & non-parametric algorithm  \n",
    "- Random forests are an example of an **ensemble method**, \n",
    "    - meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
    "\n",
    "The sum can be greater than the parts: \n",
    "- a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
    "\n",
    "We will see examples of this in the following sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivating Random Forests: Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:13:29.599199Z",
     "start_time": "2019-06-20T07:13:29.591914Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Random forests are an example of an *ensemble learner* built on decision trees.\n",
    "- For this reason we'll start by discussing decision trees.\n",
    "\n",
    "Decision trees are extremely intuitive ways to classify or label objects: \n",
    "- you simply ask a series of questions designed to zero-in on the classification.\n",
    "\n",
    "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<font size = '15pt'>Guess what is the animal I am thinking?</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "![](./img/figures/05.08-decision-tree.png)\n",
    "\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The binary splitting makes this extremely efficient: in a well-constructed tree, \n",
    "- each question will cut the number of options by approximately half, \n",
    "- very quickly narrowing the options even among a large number of classes.\n",
    "\n",
    "<font color = 'purple'>The trick comes in deciding which questions to ask at each step.</font>\n",
    "\n",
    "Using axis-aligned splits in the data: \n",
    "- each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
    "\n",
    "Let's now look at an example of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Creating a decision tree\n",
    "\n",
    "Consider the following two-dimensional data, which has one of four class labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:29:15.442656Z",
     "start_time": "2019-06-20T07:29:15.258271Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4,\n",
    "                  random_state=0, cluster_std=1.0)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A simple decision tree built on this data will iteratively split the data along one or the other axis \n",
    "- according to some quantitative criterion, and \n",
    "- at each level assign the label of the new region according to a majority vote of points within it.\n",
    "\n",
    "This figure presents a visualization of **the first four levels** of a decision tree classifier for this data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![](./img/figures/05.08-decision-tree-levels.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Notice\n",
    "\n",
    "after the each split\n",
    "\n",
    "- Nodes that contain all of one color will not be splitted again. \n",
    "- At each level *every* region is again split along one of the two features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:33:49.250451Z",
     "start_time": "2019-06-20T07:33:49.246250Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier().fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's write a quick utility function to help us visualize the output of the classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:33:53.143611Z",
     "start_time": "2019-06-20T07:33:53.109708Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
    "    ax = ax or plt.gca()\n",
    "    \n",
    "    # Plot the training points\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
    "               clim=(y.min(), y.max()), zorder=3)\n",
    "    ax.axis('tight')\n",
    "    ax.axis('off')\n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    \n",
    "    # fit the estimator\n",
    "    model.fit(X, y)\n",
    "    xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
    "                         np.linspace(*ylim, num=200))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "\n",
    "    # Create a color plot with the results\n",
    "    n_classes = len(np.unique(y))\n",
    "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
    "                           levels=np.arange(n_classes + 1) - 0.5,\n",
    "                           cmap=cmap, vmin = y.min(), vmax = y.max(),\n",
    "                           zorder=1)\n",
    "\n",
    "    ax.set(xlim=xlim, ylim=ylim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we can examine what the decision tree classification looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:33:57.645506Z",
     "start_time": "2019-06-20T07:33:57.503074Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_classifier(DecisionTreeClassifier(), X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:34:31.606139Z",
     "start_time": "2019-06-20T07:34:31.426041Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/matplotlib/contour.py:1000: UserWarning: The following kwargs were not used by contour: 'clim'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08 is found in the online appendix\n",
    "import helpers_05_08\n",
    "helpers_05_08.plot_tree_interactive(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Notice that as the depth increases, we tend to get very strangely shaped classification regions; \n",
    "- for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
    "- It's clear that this is less a result of the true, intrinsic data distribution\n",
    "- It's more a result of the particular sampling or noise properties of the data.\n",
    "\n",
    "That is, this decision tree, even at only five levels deep, is clearly **over-fitting** our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Decision trees and over-fitting\n",
    "\n",
    "Such over-fitting turns out to be a general property of decision trees: \n",
    "- it is very easy to go too deep in the tree\n",
    "    - to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
    "\n",
    "Another way to see this over-fitting is \n",
    "- to look at models trained on different subsets of the data\n",
    "\n",
    "for example, in this figure we train two different trees, each on half of the original data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![](./img/figures/05.08-decision-tree-overfitting.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "It is clear that \n",
    "- in some places, the two trees produce consistent results \n",
    "    - e.g., in the four corners\n",
    "- while in other places, the two trees give very different classifications \n",
    "    - e.g., in the regions between any two clusters\n",
    "    \n",
    "The key observation is that the inconsistencies tend to happen where the classification is less certain, \n",
    "> ### by using information from *both* of these trees, we might come up with a better result!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:40:13.136866Z",
     "start_time": "2019-06-20T07:40:12.976565Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/matplotlib/contour.py:1000: UserWarning: The following kwargs were not used by contour: 'clim'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helpers_05_08 is found in the online appendix\n",
    "import helpers_05_08\n",
    "helpers_05_08.randomized_tree_interactive(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<font size = '30pt'>Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ensembles of Estimators: Random Forests\n",
    "\n",
    "<font color = 'red'>Multiple overfitting estimators can be combined to reduce the effect of this overfitting.</font>\n",
    "This notion is  called **bagging**.\n",
    "- an ensemble method (集成学习)\n",
    "\n",
    "- Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, \n",
    "    - each of which over-fits the data, and \n",
    "    - averages the results to find a better classification.\n",
    "\n",
    "An ensemble of randomized decision trees is known as a **random forest**.\n",
    "\n",
    "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:43:57.417139Z",
     "start_time": "2019-06-20T07:43:56.897855Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
    "                        random_state=1)\n",
    "\n",
    "bag.fit(X, y)\n",
    "visualize_classifier(bag, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
    "\n",
    "In practice, decision trees are more effectively randomized by **injecting some stochasticity in how the splits are chosen**: \n",
    "- this way **all the data contributes to the fit each time**\n",
    "- but the results of the fit still have the desired randomness.\n",
    "- when determining which feature to split on, the randomized tree might select from among the **top several features**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
    "\n",
    "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
    "\n",
    "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:44:36.240793Z",
     "start_time": "2019-06-20T07:44:35.817241Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "visualize_classifier(model, X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Random Forest Regression\n",
    "\n",
    "In the previous section we considered random forests within the context of classification.\n",
    "\n",
    "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). \n",
    "- The estimator to use for this is the ``RandomForestRegressor``, and \n",
    "- the syntax is very similar to what we saw earlier.\n",
    "\n",
    "Consider the following data, drawn from the combination of a fast and slow oscillation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:46:19.060381Z",
     "start_time": "2019-06-20T07:46:18.901710Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(200)\n",
    "\n",
    "def model(x, sigma=0.3):\n",
    "    fast_oscillation = np.sin(5 * x)\n",
    "    slow_oscillation = np.sin(0.5 * x)\n",
    "    noise = sigma * rng.randn(len(x))\n",
    "\n",
    "    return slow_oscillation + fast_oscillation + noise\n",
    "\n",
    "y = model(x)\n",
    "plt.errorbar(x, y, 0.3, fmt='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Using the random forest regressor, we can find the best fit curve as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-19T10:28:32.109619Z",
     "start_time": "2019-06-19T10:28:31.751949Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(200)\n",
    "forest.fit(x[:, None], y)\n",
    "\n",
    "xfit = np.linspace(0, 10, 1000)\n",
    "yfit = forest.predict(xfit[:, None])\n",
    "ytrue = model(xfit, sigma=0)\n",
    "\n",
    "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
    "plt.plot(xfit, yfit, '-r');\n",
    "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
    "\n",
    "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example: Random Forest for Classifying Digits\n",
    "\n",
    "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
    "\n",
    "Let's use that again here to see how the random forest classifier can be used in this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:47:33.896525Z",
     "start_time": "2019-06-20T07:47:33.784699Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['DESCR', 'target_names', 'target', 'data', 'images'])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To remind us what we're looking at, we'll visualize the first few data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:47:48.102926Z",
     "start_time": "2019-06-20T07:47:45.771787Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the figure\n",
    "fig = plt.figure(figsize=(6, 6))  # figure size in inches\n",
    "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
    "\n",
    "# plot the digits: each image is 8x8 pixels\n",
    "for i in range(64):\n",
    "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
    "    \n",
    "    # label the image with the target value\n",
    "    ax.text(0, 7, str(digits.target[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can quickly classify the digits using a random forest as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:48:06.232350Z",
     "start_time": "2019-06-20T07:48:03.874539Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
    "                                                random_state=0)\n",
    "model = RandomForestClassifier(n_estimators=1000)\n",
    "model.fit(Xtrain, ytrain)\n",
    "ypred = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can take a look at the classification report for this classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:48:18.798056Z",
     "start_time": "2019-06-20T07:48:18.790288Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        38\n",
      "           1       0.98      0.98      0.98        43\n",
      "           2       0.95      1.00      0.98        42\n",
      "           3       0.98      0.96      0.97        46\n",
      "           4       0.97      1.00      0.99        37\n",
      "           5       0.96      0.94      0.95        49\n",
      "           6       1.00      1.00      1.00        52\n",
      "           7       1.00      0.96      0.98        50\n",
      "           8       0.94      0.98      0.96        46\n",
      "           9       0.96      0.96      0.96        47\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       450\n",
      "   macro avg       0.97      0.97      0.97       450\n",
      "weighted avg       0.97      0.97      0.97       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(ypred, ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And for good measure, plot the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-20T07:48:50.773887Z",
     "start_time": "2019-06-20T07:48:50.209735Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(ytest, ypred)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Summary of Random Forests\n",
    "\n",
    "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
    "Random forests are a powerful method with several advantages:\n",
    "\n",
    "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. \n",
    "    - In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
    "- The multiple trees allow for a probabilistic classification: \n",
    "    - a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
    "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
    "\n",
    "A primary disadvantage of random forests is that the results are not easily interpretable: \n",
    "- if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [In-Depth: Support Vector Machines](09.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Neural Network](09.09.neural_network.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "widgets": {
   "state": {
    "9821de3740934f48bb13d2815fcee008": {
     "views": [
      {
       "cell_index": 28
      }
     ]
    },
    "ec0cc76ff0a5482c98859b5f7feafc11": {
     "views": [
      {
       "cell_index": 22
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
